diff --git a/CMakeLists.txt b/CMakeLists.txt index 96d4dc9a55..272ba2c680 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -9,7 +9,7 @@ if (NOT CMAKE_BUILD_TYPE) endif () cmake_minimum_required(VERSION 3.14) -project(MMDeploy VERSION 1.1.0) +project(MMDeploy VERSION 1.2.0) set(CMAKE_CXX_STANDARD 17) diff --git a/csrc/mmdeploy/apis/csharp/README.md b/csrc/mmdeploy/apis/csharp/README.md index 5f3d3cb730..98ce9f73c9 100644 --- a/csrc/mmdeploy/apis/csharp/README.md +++ b/csrc/mmdeploy/apis/csharp/README.md @@ -33,14 +33,14 @@ There are two methods to build the nuget package. (*option 1*) Use the command. -If your environment is well prepared, you can just go to the `csrc\apis\csharp` folder, open a terminal and type the following command, the nupkg will be built in `csrc\apis\csharp\MMDeploy\bin\Release\MMDeployCSharp.1.1.0.nupkg`. +If your environment is well prepared, you can just go to the `csrc\apis\csharp` folder, open a terminal and type the following command, the nupkg will be built in `csrc\apis\csharp\MMDeploy\bin\Release\MMDeployCSharp.1.2.0.nupkg`. ```shell -dotnet build --configuration Release -p:Version=1.1.0 +dotnet build --configuration Release -p:Version=1.2.0 ``` (*option 2*) Open MMDeploy.sln && Build. -You can set the package-version through `Properties -> Package Version`. The default version is 1.1.0 if you don't set it. +You can set the package-version through `Properties -> Package Version`. The default version is 1.2.0 if you don't set it. If you encounter missing dependencies, follow the instructions for MSVC. diff --git a/demo/csharp/image_classification/image_classification.csproj b/demo/csharp/image_classification/image_classification.csproj index e7d722e568..60ccd9c1ea 100644 --- a/demo/csharp/image_classification/image_classification.csproj +++ b/demo/csharp/image_classification/image_classification.csproj @@ -14,7 +14,7 @@ - + diff --git a/demo/csharp/image_restorer/image_restorer.csproj b/demo/csharp/image_restorer/image_restorer.csproj index 71b97bde24..829d29b004 100644 --- a/demo/csharp/image_restorer/image_restorer.csproj +++ b/demo/csharp/image_restorer/image_restorer.csproj @@ -14,7 +14,7 @@ - + diff --git a/demo/csharp/image_segmentation/image_segmentation.csproj b/demo/csharp/image_segmentation/image_segmentation.csproj index 71b97bde24..829d29b004 100644 --- a/demo/csharp/image_segmentation/image_segmentation.csproj +++ b/demo/csharp/image_segmentation/image_segmentation.csproj @@ -14,7 +14,7 @@ - + diff --git a/demo/csharp/object_detection/object_detection.csproj b/demo/csharp/object_detection/object_detection.csproj index 583634ba7d..5bdde1ec61 100644 --- a/demo/csharp/object_detection/object_detection.csproj +++ b/demo/csharp/object_detection/object_detection.csproj @@ -14,7 +14,7 @@ - + diff --git a/demo/csharp/ocr_detection/ocr_detection.csproj b/demo/csharp/ocr_detection/ocr_detection.csproj index 71b97bde24..829d29b004 100644 --- a/demo/csharp/ocr_detection/ocr_detection.csproj +++ b/demo/csharp/ocr_detection/ocr_detection.csproj @@ -14,7 +14,7 @@ - + diff --git a/demo/csharp/ocr_recognition/ocr_recognition.csproj b/demo/csharp/ocr_recognition/ocr_recognition.csproj index 71b97bde24..829d29b004 100644 --- a/demo/csharp/ocr_recognition/ocr_recognition.csproj +++ b/demo/csharp/ocr_recognition/ocr_recognition.csproj @@ -14,7 +14,7 @@ - + diff --git a/demo/csharp/pose_detection/pose_detection.csproj b/demo/csharp/pose_detection/pose_detection.csproj index 71b97bde24..829d29b004 100644 --- a/demo/csharp/pose_detection/pose_detection.csproj +++ b/demo/csharp/pose_detection/pose_detection.csproj @@ -14,7 +14,7 @@ - + diff --git a/docs/en/01-how-to-build/build_from_docker.md b/docs/en/01-how-to-build/build_from_docker.md index f98c6b49a7..d319635ef8 100644 --- a/docs/en/01-how-to-build/build_from_docker.md +++ b/docs/en/01-how-to-build/build_from_docker.md @@ -5,7 +5,7 @@ This document guides how to install mmdeploy with [Docker](https://docs.docker.c ## Get prebuilt docker images MMDeploy provides prebuilt docker images for the convenience of its users on [Docker Hub](https://hub.docker.com/r/openmmlab/mmdeploy). The docker images are built on -the latest and released versions. For instance, the image with tag `openmmlab/mmdeploy:ubuntu20.04-cuda11.3-mmdeploy` is built on the latest mmdeploy and the image with tag `openmmlab/mmdeploy:ubuntu20.04-cuda11.3-mmdeploy1.1.0` is for `mmdeploy==1.1.0`. +the latest and released versions. For instance, the image with tag `openmmlab/mmdeploy:ubuntu20.04-cuda11.3-mmdeploy` is built on the latest mmdeploy and the image with tag `openmmlab/mmdeploy:ubuntu20.04-cuda11.3-mmdeploy1.2.0` is for `mmdeploy==1.2.0`. The specifications of the Docker Image are shown below. | Item | Version | diff --git a/docs/en/02-how-to-run/prebuilt_package_windows.md b/docs/en/02-how-to-run/prebuilt_package_windows.md index 5d67c55e97..77057caf05 100644 --- a/docs/en/02-how-to-run/prebuilt_package_windows.md +++ b/docs/en/02-how-to-run/prebuilt_package_windows.md @@ -21,7 +21,7 @@ ______________________________________________________________________ -This tutorial takes `mmdeploy-1.1.0-windows-amd64.zip` and `mmdeploy-1.1.0-windows-amd64-cuda11.3.zip` as examples to show how to use the prebuilt packages. The former support onnxruntime cpu inference, the latter support onnxruntime-gpu and tensorrt inference. +This tutorial takes `mmdeploy-1.2.0-windows-amd64.zip` and `mmdeploy-1.2.0-windows-amd64-cuda11.3.zip` as examples to show how to use the prebuilt packages. The former support onnxruntime cpu inference, the latter support onnxruntime-gpu and tensorrt inference. The directory structure of the prebuilt package is as follows, where the `dist` folder is about model converter, and the `sdk` folder is related to model inference. @@ -81,8 +81,8 @@ In order to use `ONNX Runtime` backend, you should also do the following steps. 5. Install `mmdeploy` (Model Converter) and `mmdeploy_runtime` (SDK Python API). ```bash - pip install mmdeploy==1.1.0 - pip install mmdeploy-runtime==1.1.0 + pip install mmdeploy==1.2.0 + pip install mmdeploy-runtime==1.2.0 ``` :point_right: If you have installed it before, please uninstall it first. @@ -100,7 +100,7 @@ In order to use `ONNX Runtime` backend, you should also do the following steps. ![sys-path](https://user-images.githubusercontent.com/16019484/181463801-1d7814a8-b256-46e9-86f2-c08de0bc150b.png) :exclamation: Restart powershell to make the environment variables setting take effect. You can check whether the settings are in effect by `echo $env:PATH`. -8. Download SDK C/cpp Library mmdeploy-1.1.0-windows-amd64.zip +8. Download SDK C/cpp Library mmdeploy-1.2.0-windows-amd64.zip ### TensorRT @@ -109,8 +109,8 @@ In order to use `TensorRT` backend, you should also do the following steps. 5. Install `mmdeploy` (Model Converter) and `mmdeploy_runtime` (SDK Python API). ```bash - pip install mmdeploy==1.1.0 - pip install mmdeploy-runtime-gpu==1.1.0 + pip install mmdeploy==1.2.0 + pip install mmdeploy-runtime-gpu==1.2.0 ``` :point_right: If you have installed it before, please uninstall it first. @@ -129,7 +129,7 @@ In order to use `TensorRT` backend, you should also do the following steps. 7. Install pycuda by `pip install pycuda` -8. Download SDK C/cpp Library mmdeploy-1.1.0-windows-amd64-cuda11.3.zip +8. Download SDK C/cpp Library mmdeploy-1.2.0-windows-amd64-cuda11.3.zip ## Model Convert @@ -141,7 +141,7 @@ After preparation work, the structure of the current working directory should be ``` .. -|-- mmdeploy-1.1.0-windows-amd64 +|-- mmdeploy-1.2.0-windows-amd64 |-- mmpretrain |-- mmdeploy `-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth @@ -189,7 +189,7 @@ After installation of mmdeploy-tensorrt prebuilt package, the structure of the c ``` .. -|-- mmdeploy-1.1.0-windows-amd64-cuda11.3 +|-- mmdeploy-1.2.0-windows-amd64-cuda11.3 |-- mmpretrain |-- mmdeploy `-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth @@ -252,8 +252,8 @@ The structure of current working directory： ``` . -|-- mmdeploy-1.1.0-windows-amd64 -|-- mmdeploy-1.1.0-windows-amd64-cuda11.3 +|-- mmdeploy-1.2.0-windows-amd64 +|-- mmdeploy-1.2.0-windows-amd64-cuda11.3 |-- mmpretrain |-- mmdeploy |-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth @@ -324,7 +324,7 @@ The following describes how to use the SDK's C API for inference It is recommended to use `CMD` here. - Under `mmdeploy-1.1.0-windows-amd64\\example\\cpp\\build\\Release` directory： + Under `mmdeploy-1.2.0-windows-amd64\\example\\cpp\\build\\Release` directory： ``` .\image_classification.exe cpu C:\workspace\work_dir\onnx\resnet\ C:\workspace\mmpretrain\demo\demo.JPEG @@ -344,7 +344,7 @@ The following describes how to use the SDK's C API for inference It is recommended to use `CMD` here. - Under `mmdeploy-1.1.0-windows-amd64-cuda11.3\\example\\cpp\\build\\Release` directory + Under `mmdeploy-1.2.0-windows-amd64-cuda11.3\\example\\cpp\\build\\Release` directory ``` .\image_classification.exe cuda C:\workspace\work_dir\trt\resnet C:\workspace\mmpretrain\demo\demo.JPEG diff --git a/docs/en/get_started.md b/docs/en/get_started.md index 257002984a..760e076c8b 100644 --- a/docs/en/get_started.md +++ b/docs/en/get_started.md @@ -118,14 +118,14 @@ Take the latest precompiled package as example, you can install it as follows: ```shell # 1. install MMDeploy model converter -pip install mmdeploy==1.1.0 +pip install mmdeploy==1.2.0 # 2. install MMDeploy sdk inference # you can install one to install according whether you need gpu inference # 2.1 support onnxruntime -pip install mmdeploy-runtime==1.1.0 +pip install mmdeploy-runtime==1.2.0 # 2.2 support onnxruntime-gpu, tensorrt -pip install mmdeploy-runtime-gpu==1.1.0 +pip install mmdeploy-runtime-gpu==1.2.0 # 3. install inference engine # 3.1 install TensorRT @@ -230,9 +230,9 @@ result = inference_model( You can directly run MMDeploy demo programs in the precompiled package to get inference results. ```shell -wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.1.0/mmdeploy-1.1.0-linux-x86_64-cuda11.3.tar.gz -tar xf mmdeploy-1.1.0-linux-x86_64-cuda11.3 -cd mmdeploy-1.1.0-linux-x86_64-cuda11.3 +wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.2.0/mmdeploy-1.2.0-linux-x86_64-cuda11.3.tar.gz +tar xf mmdeploy-1.2.0-linux-x86_64-cuda11.3 +cd mmdeploy-1.2.0-linux-x86_64-cuda11.3 # run python demo python example/python/object_detection.py cuda ../mmdeploy_model/faster-rcnn ../mmdetection/demo/demo.jpg # run C/C++ demo diff --git a/docs/zh_cn/01-how-to-build/build_from_docker.md b/docs/zh_cn/01-how-to-build/build_from_docker.md index 3e7fc36f86..4813dad195 100644 --- a/docs/zh_cn/01-how-to-build/build_from_docker.md +++ b/docs/zh_cn/01-how-to-build/build_from_docker.md @@ -4,8 +4,8 @@ ## 获取镜像 -为了方便用户，mmdeploy在[Docker Hub](https://hub.docker.com/r/openmmlab/mmdeploy)上提供了多个版本的镜像，例如对于`mmdeploy==1.1.0`， -其镜像标签为`openmmlab/mmdeploy:ubuntu20.04-cuda11.3-mmdeploy1.1.0`，而最新的镜像标签为`openmmlab/mmdeploy:ubuntu20.04-cuda11.3-mmdeploy`。 +为了方便用户，mmdeploy在[Docker Hub](https://hub.docker.com/r/openmmlab/mmdeploy)上提供了多个版本的镜像，例如对于`mmdeploy==1.2.0`， +其镜像标签为`openmmlab/mmdeploy:ubuntu20.04-cuda11.3-mmdeploy1.2.0`，而最新的镜像标签为`openmmlab/mmdeploy:ubuntu20.04-cuda11.3-mmdeploy`。 镜像相关规格信息如下表所示： | Item | Version | diff --git a/docs/zh_cn/02-how-to-run/prebuilt_package_windows.md b/docs/zh_cn/02-how-to-run/prebuilt_package_windows.md index 2a694f89a0..1abf99d0b6 100644 --- a/docs/zh_cn/02-how-to-run/prebuilt_package_windows.md +++ b/docs/zh_cn/02-how-to-run/prebuilt_package_windows.md @@ -23,7 +23,7 @@ ______________________________________________________________________ 目前，`MMDeploy`在`Windows`平台下提供`cpu`以及`cuda`两种Device的预编译包，其中`cpu`版支持使用onnxruntime cpu进行推理，`cuda`版支持使用onnxruntime-gpu以及tensorrt进行推理，可以从[Releases](https://github.com/open-mmlab/mmdeploy/releases)获取。。 -本篇教程以`mmdeploy-1.1.0-windows-amd64.zip`和`mmdeploy-1.1.0-windows-amd64-cuda11.3.zip`为例，展示预编译包的使用方法。 +本篇教程以`mmdeploy-1.2.0-windows-amd64.zip`和`mmdeploy-1.2.0-windows-amd64-cuda11.3.zip`为例，展示预编译包的使用方法。 为了方便使用者快速上手，本教程以分类模型(mmpretrain)为例，展示两种预编译包的使用方法。 @@ -89,8 +89,8 @@ ______________________________________________________________________ 5. 安装`mmdeploy`（模型转换）以及`mmdeploy_runtime`（模型推理Python API）的预编译包 ```bash - pip install mmdeploy==1.1.0 - pip install mmdeploy-runtime==1.1.0 + pip install mmdeploy==1.2.0 + pip install mmdeploy-runtime==1.2.0 ``` :point_right: 如果之前安装过，需要先卸载后再安装。 @@ -108,7 +108,7 @@ ______________________________________________________________________ ![sys-path](https://user-images.githubusercontent.com/16019484/181463801-1d7814a8-b256-46e9-86f2-c08de0bc150b.png) :exclamation: 重启powershell让环境变量生效，可以通过 echo $env:PATH 来检查是否设置成功。 -8. 下载 SDK C/cpp Library mmdeploy-1.1.0-windows-amd64.zip +8. 下载 SDK C/cpp Library mmdeploy-1.2.0-windows-amd64.zip ### TensorRT @@ -117,8 +117,8 @@ ______________________________________________________________________ 5. 安装`mmdeploy`（模型转换）以及`mmdeploy_runtime`（模型推理Python API）的预编译包 ```bash - pip install mmdeploy==1.1.0 - pip install mmdeploy-runtime-gpu==1.1.0 + pip install mmdeploy==1.2.0 + pip install mmdeploy-runtime-gpu==1.2.0 ``` :point_right: 如果之前安装过，需要先卸载后再安装 @@ -137,7 +137,7 @@ ______________________________________________________________________ 7. 安装pycuda `pip install pycuda` -8. 下载 SDK C/cpp Library mmdeploy-1.1.0-windows-amd64-cuda11.3.zip +8. 下载 SDK C/cpp Library mmdeploy-1.2.0-windows-amd64-cuda11.3.zip ## 模型转换 @@ -149,7 +149,7 @@ ______________________________________________________________________ ``` .. -|-- mmdeploy-1.1.0-windows-amd64 +|-- mmdeploy-1.2.0-windows-amd64 |-- mmpretrain |-- mmdeploy `-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth @@ -197,7 +197,7 @@ export2SDK(deploy_cfg, model_cfg, work_dir, pth=model_checkpoint, device=device) ``` .. -|-- mmdeploy-1.1.0-windows-amd64-cuda11.3 +|-- mmdeploy-1.2.0-windows-amd64-cuda11.3 |-- mmpretrain |-- mmdeploy `-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth @@ -260,8 +260,8 @@ export2SDK(deploy_cfg, model_cfg, work_dir, pth=model_checkpoint, device=device) ``` . -|-- mmdeploy-1.1.0-windows-amd64 -|-- mmdeploy-1.1.0-windows-amd64-cuda11.3 +|-- mmdeploy-1.2.0-windows-amd64 +|-- mmdeploy-1.2.0-windows-amd64-cuda11.3 |-- mmpretrain |-- mmdeploy |-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth @@ -340,7 +340,7 @@ python .\mmdeploy\demo\python\image_classification.py cpu .\work_dir\onnx\resnet 这里建议使用cmd，这样如果exe运行时如果找不到相关的dll的话会有弹窗 - 在mmdeploy-1.1.0-windows-amd64\\example\\cpp\\build\\Release目录下： + 在mmdeploy-1.2.0-windows-amd64\\example\\cpp\\build\\Release目录下： ``` .\image_classification.exe cpu C:\workspace\work_dir\onnx\resnet\ C:\workspace\mmpretrain\demo\demo.JPEG @@ -360,7 +360,7 @@ python .\mmdeploy\demo\python\image_classification.py cpu .\work_dir\onnx\resnet 这里建议使用cmd，这样如果exe运行时如果找不到相关的dll的话会有弹窗 - 在mmdeploy-1.1.0-windows-amd64-cuda11.3\\example\\cpp\\build\\Release目录下： + 在mmdeploy-1.2.0-windows-amd64-cuda11.3\\example\\cpp\\build\\Release目录下： ``` .\image_classification.exe cuda C:\workspace\work_dir\trt\resnet C:\workspace\mmpretrain\demo\demo.JPEG diff --git a/docs/zh_cn/get_started.md b/docs/zh_cn/get_started.md index b284d15e0c..20cf2dc665 100644 --- a/docs/zh_cn/get_started.md +++ b/docs/zh_cn/get_started.md @@ -113,14 +113,14 @@ mim install "mmcv>=2.0.0rc2" ```shell # 1. 安装 MMDeploy 模型转换工具（含trt/ort自定义算子） -pip install mmdeploy==1.1.0 +pip install mmdeploy==1.2.0 # 2. 安装 MMDeploy SDK推理工具 # 根据是否需要GPU推理可任选其一进行下载安装 # 2.1 支持 onnxruntime 推理 -pip install mmdeploy-runtime==1.1.0 +pip install mmdeploy-runtime==1.2.0 # 2.2 支持 onnxruntime-gpu tensorrt 推理 -pip install mmdeploy-runtime-gpu==1.1.0 +pip install mmdeploy-runtime-gpu==1.2.0 # 3. 安装推理引擎 # 3.1 安装推理引擎 TensorRT @@ -223,10 +223,10 @@ result = inference_model( 你可以直接运行预编译包中的 demo 程序，输入 SDK Model 和图像，进行推理，并查看推理结果。 ```shell -wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.1.0/mmdeploy-1.1.0-linux-x86_64-cuda11.3.tar.gz -tar xf mmdeploy-1.1.0-linux-x86_64-cuda11.3 +wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.2.0/mmdeploy-1.2.0-linux-x86_64-cuda11.3.tar.gz +tar xf mmdeploy-1.2.0-linux-x86_64-cuda11.3 -cd mmdeploy-1.1.0-linux-x86_64-cuda11.3 +cd mmdeploy-1.2.0-linux-x86_64-cuda11.3 # 运行 python demo python example/python/object_detection.py cuda ../mmdeploy_model/faster-rcnn ../mmdetection/demo/demo.jpg # 运行 C/C++ demo diff --git a/mmdeploy/version.py b/mmdeploy/version.py index 1dbb5cc815..a7c77d5ee5 100644 --- a/mmdeploy/version.py +++ b/mmdeploy/version.py @@ -1,7 +1,7 @@ # Copyright (c) OpenMMLab. All rights reserved. from typing import Tuple -__version__ = '1.1.0' +__version__ = '1.2.0' short_version = __version__ diff --git a/tools/package_tools/packaging/mmdeploy_runtime/version.py b/tools/package_tools/packaging/mmdeploy_runtime/version.py index 2c9959a904..a3c6ea2b01 100644 --- a/tools/package_tools/packaging/mmdeploy_runtime/version.py +++ b/tools/package_tools/packaging/mmdeploy_runtime/version.py @@ -1,2 +1,2 @@ # Copyright (c) OpenMMLab. All rights reserved. -__version__ = '1.1.0' +__version__ = '1.2.0'